##           used (Mb) gc trigger  (Mb) max used (Mb)
## Ncells 1027317 54.9    2127068 113.6  1296870 69.3
## Vcells 1721619 13.2    8388608  64.0  2221721 17.0

Definitions

Ciudad Trip Collection Diary Frequency Trip_duration
Bogota2019 Moving from one part to another with a specific reason/motive, a definite hour of start and end, a mode of transport, and a duration greater than 3 minutes. Or moving from one part to another with reason/motive work or study of any duration Trips made the day of reference, i.e., the day before the survey (from 4am yesterday to 4am today). Surveys made from Monday to Sunday (according to the dataset). No diary, questionnaire Yes. Monday to Sunday or Occasional Already in the dataset
Mexico Moving from one part to another with a specific reason/motive, using one or multiples modes of transport Trips made during the week (Tuesday, Wednesday, Thursday) and in Saturdays (weekends) Yes, one for weekdays and one for saturdays No I calculated it
Cali Moving from one part to another with a specific reason/motive and a duration longer than 3 minutes. Or moving from one part to another with reason/motive work or study of any duration Trips made the day of reference, i.e., the day before the survey (from 4am yesterday to 4am today). Surveys made from Monday to Sunday (according to the dataset). No diary, questionnaire No Already in the dataset
Medellin Couldn’t find the definition Trips made the day of reference, i.e., last 24 hours No diary, questionnaire Yes, daily, weekly, monthly, yearly. I calculated it
Santiago Any movement carried out on public roads with a purpose determined, between two places (origin and destination) at a certain time of day; It can be carried out in several modes of transport and consist of one or more stages Trips made in working days (regular season), in weekends (regular season) and in working days(summer season) Yes, a day was randomly assigned to each respondent. No Already in the dataset
Sao Paulo Moving for a specific reason between two specific points (origin and destination), using one or more modes of transport. Walking trips where the reason for the trip is work or school, regardless of the distance travelled; or the distance covered is more than 500 mts for other reasons. Trips made the day before the survey (from 4am yesterday to 3:59am today). No diary, questionnaire No, but it asks the day of the week when the trip was made Already in the dataset
Rosario A trip of 4 blocks or more (from the questionnaire). Trips made the working day before the survey (from 4am yesterday to 4am today). No diary, questionnaire No Already in the dataset
Lima only people over 6 years old. … No diary, questionnaire No I have to calculate it
Montevideo … Trips made the day before the survey (from 4am yesterday to 4am today). No diary, questionnaire Yes, 5 days a week, 3-4 days a week, 1-2 days a week, 2-3 days a month, once a month I have to calculate it

Summary table

Bogota2015 Bogota2019 Bogota2019_longer15 Mexico Mexico_weekdays Mexico_weekends Medellin Cali Santiago
Min. 0.0 0.0 0.0 0.0 1 1.0 1.0 0.0 0.0
1st Qu. 14.0 15.0 25.0 20.0 15 15.0 15.0 10.0 15.0
Median 32.2 30.0 45.0 43.0 30 30.0 30.0 25.0 30.0
Mean 39.7 50.6 58.6 52.4 43 43.2 33.7 42.2 36.9
3rd Qu. 61.6 60.0 75.0 75.0 60 60.0 45.0 45.0 50.0
Max. 553.7 1110.0 1110.0 1200.0 840 735.0 600.0 1282.0 1335.0
NA’s 22515.0 10319.0 13899.0 0.0 17964 37916.0 6494.0 12618.0 14066.0

Density plot

These plots are interactive so we can zoom in and out, and select cities.

g1 <- ggplotly(
  ggplot() + 
    geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 , 
                          data = bogota_2015) +
    geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 , 
               data = bogota_2019) +
    geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 , 
               data = bogota_2019_longer15) +
    geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 , 
                 data = mexico) +
    geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 , 
                 data = mexico_weekdays) + 
    geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 , 
                 data = mexico_weekends) +
    geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 , 
                 data = medellin) +
    geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 , 
                 data = cali) +
    geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 , 
                 data = santiago) 
    )
## Warning: Removed 22515 rows containing non-finite values (stat_density).
## Warning: Removed 10319 rows containing non-finite values (stat_density).
## Warning: Removed 13899 rows containing non-finite values (stat_density).
## Warning: Removed 17964 rows containing non-finite values (stat_density).
## Warning: Removed 37916 rows containing non-finite values (stat_density).
## Warning: Removed 6494 rows containing non-finite values (stat_density).
## Warning: Removed 12618 rows containing non-finite values (stat_density).
## Warning: Removed 14066 rows containing non-finite values (stat_density).
#htmlwidgets::saveWidget(g1, "g1.html")
#display_html('')

Density plot by mode

Bogota 2015

ggplotly(ggplot() + 
    geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                 alpha = .3 , data = bogota_2015))
## Warning: Removed 22515 rows containing non-finite values (stat_density).
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Bogota 2019

ggplotly(ggplot() + 
    geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                 alpha = .3 , data = bogota_2019))
## Warning: Removed 10319 rows containing non-finite values (stat_density).

Bogota 2019 walking trips longer than 15 minutes

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = bogota_2019_longer15))
## Warning: Removed 13899 rows containing non-finite values (stat_density).

Mexico

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = mexico))

Mexico weekdays

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = mexico_weekdays))
## Warning: Removed 17964 rows containing non-finite values (stat_density).

Mexico weekends

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = mexico_weekends))
## Warning: Removed 37916 rows containing non-finite values (stat_density).

Medellin

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = medellin))
## Warning: Removed 6494 rows containing non-finite values (stat_density).

Cali

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = cali))
## Warning: Removed 12618 rows containing non-finite values (stat_density).

Santiago

ggplotly(ggplot() + 
           geom_density(aes(trip_duration, group = trip_mode, fill = trip_mode), 
                        alpha = .3 , data = santiago))
## Warning: Removed 14066 rows containing non-finite values (stat_density).

Comparison of walking trips

ggplotly(
  ggplot() + 
    geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 , 
                 data = bogota_2015 %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 , 
                 data = bogota_2019 %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 , 
                 data = bogota_2019_longer15 %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 , 
                 data = mexico %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 , 
                 data = mexico_weekdays %>% 
                   filter(trip_mode == "walk")) + 
    geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 , 
                 data = mexico_weekends %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 , 
                 data = medellin %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 , 
                 data = cali %>% 
                   filter(trip_mode == "walk")) +
    geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 , 
                 data = santiago %>% 
                   filter(trip_mode == "walk")) 
)
## Warning: Removed 4 rows containing non-finite values (stat_density).
## Warning: Removed 1 rows containing non-finite values (stat_density).
## Warning: Removed 398 rows containing non-finite values (stat_density).
## Warning: Removed 2 rows containing non-finite values (stat_density).

Comparison of cycling trips

ggplotly(
  ggplot() + 
    geom_density(aes(trip_duration, fill = "Bogota2015"), alpha = .3 , 
                 data = bogota_2015 %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Bogota2019"), alpha = .3 , 
                 data = bogota_2019 %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Bogota2019_longer"), alpha = .3 , 
                 data = bogota_2019_longer15 %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Mexico"), alpha = .3 , 
                 data = mexico %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Mexico_weekdays"), alpha = .3 , 
                 data = mexico_weekdays %>% 
                   filter(trip_mode == "bicycle")) + 
    geom_density(aes(trip_duration, fill = "Mexico_weekends"), alpha = .3 , 
                 data = mexico_weekends %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Medellin"), alpha = .3 , 
                 data = medellin %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Cali"), alpha = .3 , 
                 data = cali %>% 
                   filter(trip_mode == "bicycle")) +
    geom_density(aes(trip_duration, fill = "Santiago"), alpha = .3 , 
                 data = santiago %>% 
                   filter(trip_mode == "bicycle")) 
)
## Warning: Removed 40 rows containing non-finite values (stat_density).